The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the curation and archival storage of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2018 (build 3.4.164), BioGRID contains records for 1 598 688 biological interactions manually annotated from 55 809 publications for 71 species, as classified by an updated set of controlled vocabularies for experimental detection methods. BioGRID also houses records for >700 000 post-translational modification sites. BioGRID now captures chemical interaction data, including chemical–protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature. A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene–phenotype and gene–gene relationships. An extension of the BioGRID resource called the Open Repository for CRISPR Screens (ORCS) database (https://orcs.thebiogrid.org) currently contains over 500 genome-wide screens carried out in human or mouse cell lines. All data in BioGRID is made freely available without restriction, is directly downloadable in standard formats and can be readily incorporated into existing applications via our web service platforms. BioGRID data are also freely distributed through partner model organism databases and meta-databases.

We used synthetic lethal high-throughput screening to interrogate 23,550 compounds for their ability to kill engineered tumorigenic cells but not their isogenic normal cell counterparts. We identified known and novel compounds with genotype-selective activity, including doxorubicin, daunorubicin, mitoxantrone, camptothecin, sangivamycin, echinomycin, bouvardin, NSC146109, and a novel compound that we named erastin. These compounds have increased activity in the presence of hTERT, the SV40 large and small T oncoproteins, the human papillomavirus type 16 (HPV) E6 and E7 oncoproteins, and oncogenic HRAS. We found that overexpressing hTERT and either E7 or LT increased expression of topoisomerase 2alpha and that overexpressing RAS(V12) and ST both increased expression of topoisomerase 1 and sensitized cells to a nonapoptotic cell death process initiated by erastin.

The BioGRID (Biological General Repository for Interaction Datasets, http://thebiogrid.org) is an open‐access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low‐throughput studies and large high‐throughput datasets. BioGRID also captures protein post‐translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built‐in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin‐proteasome system, as well as specific disease areas, such as for the SARS‐CoV‐2 virus that causes COVID‐19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, http://orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome‐wide CRISPR/Cas9‐based genetic screens. BioGRID‐ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta‐databases.

SummaryHuman glioblastomas (GBMs) harbour a subpopulation of glioblastoma stem cells (GSCs) that drive tumourigenesis. However, the origin of intra-tumoural functional heterogeneity between GBM cells remains poorly understood. Here we study the clonal evolution of barcoded GBM cells in an unbiased way following serial xenotransplantation to define their individual fate behaviours. Independent of an evolving mutational signature, we show that the growth of GBM clones in vivo is consistent with a remarkably neutral process involving a conserved proliferative hierarchy rooted in GSCs. In this model, slow-cycling stem-like cells give rise to a more rapidly cycling progenitor population with extensive self-maintenance capacity, that in turn generates non-proliferative cells. We also identify rare “outlier” clones that deviate from these dynamics, and further show that chemotherapy facilitates the expansion of pre-existing drug-resistant GSCs. Finally, we show that functionally distinct GSCs can be separately targeted using epigenetic compounds, suggesting new avenues for GBM targeted therapy.

SUMMARY Glioblastomas (GBM) grow in a rich neurochemical milieu, but the impact of neurochemicals on GBM growth is largely unexplored. We interrogated 680 neurochemical compounds in patient-derived GBM neural stem cells (GNS) to determine the effects on proliferation and survival. Compounds that modulate dopaminergic, serotonergic, and cholinergic signaling pathways selectively affected GNS growth. In particular, dopamine receptor D4 (DRD4) antagonists selectively inhibited GNS growth and promoted differentiation of normal neural stem cells. DRD4 antagonists inhibited the downstream effectors PDGFRβ, ERK1/2, and mTOR and disrupted the autophagy-lysosomal pathway, leading to accumulation of autophagic vacuoles followed by G0/G1 arrest and apoptosis. These results demonstrate a role for neurochemical pathways in governing GBM stem cell proliferation and suggest therapeutic approaches for GBM.

Summary The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4915 compounds. This approach uncovered 1221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the global genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naïve Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity towards human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.

Developmental signal transduction pathways act diversely, with context-dependent roles across systems and disease types. Glioblastomas (GBMs), which are the poorest prognosis primary brain cancers, strongly resemble developmental systems, but these growth processes have not been exploited therapeutically, likely in part due to the extreme cellular and genetic heterogeneity observed in these tumors. The role of Wnt/βcatenin signaling in GBM stem cell (GSC) renewal and fate decisions remains controversial. Here, we report context-specific actions of Wnt/ βcatenin signaling in directing cellular fate specification and renewal. A subset of primary GBM-derived stem cells requires Wnt proteins for self-renewal, and this subset specifically relies on Wnt/βcatenin signaling for enhanced tumor burden in xenograft models. In an orthotopic Wnt reporter model, Wnt hi GBM cells (which exhibit high levels of βcatenin signaling) are a faster-cycling, highly self-renewing stem cell pool. In contrast, Wnt lo cells (with low levels of signaling) are slower cycling and have decreased self-renewing potential. Dual inhibition of Wnt/βcatenin and Notch signaling in GSCs that express high levels of the proneural transcription factor ASCL1 leads to robust neuronal differentiation and inhibits clonogenic potential. Our work identifies new contexts for Wnt modulation for targeting stem cell differentiation and self-renewal in GBM heterogeneity, which deserve further exploration therapeutically.

Mills and colleagues argue that the challenge of enrolling women into HIV vaccine trials must be overcome in order for trials to be considered ethical, valid, and generalizable.